The water footprint and nutritional implications of diet change in Mexico: a principal component analysis

The environmental impact of beef and ultra-processed food consumption in Brazil

OBJECTIVE

This study evaluated the independent and combined environmental impacts of the consumption of beef and ultra-processed foods in Brazil.

DESIGN

Cross-sectional study.

SETTING

Brazil.

PARTICIPANTS

We used food purchases data from a national household budget survey conducted between July 2017 and July 2018, representing all Brazilian households. Food purchases were converted into energy, carbon footprints and water footprints. Multiple linear regression models were used to assess the association between quintiles of beef and ultra-processed foods in total energy purchases and the environmental footprints, controlling for sociodemographic variables.

RESULTS

Both beef and ultra-processed foods had a significant linear association with carbon and water footprints (P < 0·01) in crude and adjusted models. In the crude upper quintile of beef purchases, carbon and water footprints were 47·7 % and 30·8 % higher, respectively, compared to the lower quintile. The upper quintile of ultra-processed food purchases showed carbon and water footprints 14·4 % and 22·8 % higher, respectively, than the lower quintile. The greatest reduction in environmental footprints would occur when both beef and ultra-processed food purchases are decreased, resulting in a 21·1 % reduction in carbon footprint and a 20·0 % reduction in water footprint.

CONCLUSIONS

Although the environmental footprints associated with beef consumption are higher, dietary patterns with lower consumption of beef and ultra-processed foods combined showed the greatest reduction in carbon and water footprints in Brazil. The high consumption of beef and ultra-processed foods is harmful to human health, as well as to the environment; thus, their reduction is beneficial to both.

Evaluation of personal water footprint components in Turkey: factors associated with obesity and food consumption

This study aimed to evaluate the personal water footprint individuals, and was conducted with 3431 individuals İbetween 18-95 years. A questionnaire form included questions about demographic characteristics, anthropometric measurements, and food consumption was applied. The "Extended Water Footprint Calculator" was used to evaluate the personal water footprint, and various questions were asked to determine the components of food consumption, domestic water, and industrial water use. The average age of the participants was 34.6 ± 13.93 years. The total water footprint of the male was significantly higher than the female. The total water footprint of individuals between the ages of 41-64 was higher than other age groups, and also obese individuals had the highest total water footprint (p < 0.05). Meat and meat products contributed the most to the total water footprint. Having a normal body weight and adopting more sustainable dietary patterns should be emphasized because of their impacts on the environment and health.

Sustainable-psycho-nutritional intervention programme for a sustainable diet (the 'NutriSOS' study) and its effects on eating behaviour, diet quality, nutritional status, physical activity, metabolic biomarkers, gut microbiota and water and carbon footprints in Mexican population: study protocol of an mHealth randomised controlled trial

Mexico is going through an environmental and nutritional crisis related to unsustainable dietary behaviours. Sustainable diets could solve both problems together. This study protocol aims to develop a three-stage, 15-week mHealth randomised controlled trial of a sustainable-psycho-nutritional intervention programme to promote Mexican population adherence to a sustainable diet and to evaluate its effects on health and environmental outcomes. In stage 1, the programme will be designed using the sustainable diets, behaviour change wheel and capability, opportunity, motivation, and behaviour (COM-B) models. A sustainable food guide, recipes, meal plans and a mobile application will be developed. In stage 2, the intervention will be implemented for 7 weeks, and a 7-week follow-up period in a young Mexican adults (18-35 years) sample, randomly divided (1:1 ratio) into a control group (n 50) and an experimental group (n 50), will be divided into two arms at week 8. Outcomes will include health, nutrition, environment, behaviour and nutritional-sustainable knowledge. Additionally, socio-economics and culture will be considered. Thirteen behavioural objectives will be included using successive approaches in online workshops twice a week. The population will be monitored using the mobile application consisting of behavioural change techniques. In stage 3, the effects of the intervention will be assessed using mixed-effects models on dietary intake and quality, nutritional status, physical activity, metabolic biomarkers (serum glucose and lipid profile), gut microbiota composition and dietary water and carbon footprints of the evaluated population. Improvements in health outcomes and a decrease in dietary water and carbon footprints are expected.

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

The economic development of various regions is influenced by high-quality population mobility. The research object of this article is the employment mobility data of “Double First-Class” university graduates from 2014 to 2019; the subsequent analysis is based on these data. First, this paper summarizes the current state of university graduates’ employment mobility. Second, this paper employs the fixed-effect model and PCA method to conclude that economic factors are the primary factors influencing university graduates’ employment mobility. Finally, based on the nonlinear, small sample, and high-dimensional characteristics of university graduates’ employment mobility data, this paper employs the random forest and BP neural network methods to build a prediction model for university graduates’ employment retention rate. The results show that the BP neural network model outperforms the random forest model in terms of prediction accuracy. The BP neural network model can accurately predict the employment retention rate of “Double First-Class” university graduates, which can guide the reasonable mobility of university graduates and provide a reference for government universities and individuals to make decisions.